Methods, systems, and computer readable media for adaptive packet filtering

ABSTRACT

Methods, systems, and computer readable media for adaptive packet filtering are disclosed. One method includes identifying at least one subset of rules and an ordered set of firewall packet filtering rules that defines a firewall policy such that the subset contains disjoint rules. Disjoint rules are defined as rules whose order can be changed without changing integrity of the firewall policy. Rules in the subset are sorted to statistically decrease the number of comparisons that will be applied to each packet that a firewall encounters. Packets are filtered at the firewall using the sorted rules in the subset by comparing each packet to each of the sorted rules in the subset until the packet is allowed or denied and ceasing the comparing for the packet in response to the packet being allowed or denied and thereby achieving sub-linear searching for packets filtered using the sorted rules in the subset.

PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No. 12/871,806, filed Aug. 30, 2010, which claims the benefit of U.S. Provisional Patent Application Ser. No. 61/237,974, filed Aug. 28, 2009; the disclosures of which are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The subject matter described herein relates to network firewall filtering. More particularly, the subject matter described herein relates to methods, systems, and computer readable media for adaptive packet filtering.

BACKGROUND

A firewall generally processes a packet against a list of ordered rules to find the first rule match. The list of ordered rules represents an aggregate security policy, and arbitrarily changing the order of the rules can result in a violation of the aggregate security policy. The Wake Forest University (WFU) techniques described in U.S. patent application publication nos. 2006/0248580 and 2006/0195896 provide the methods to optimally reorder the list while preserving the aggregate security policy, thereby improving the performance of the firewall. The WFU techniques also include methods to break apart rules into functionally independent lists containing (groups of) dependent rules such that a function parallel firewall can simultaneously process one packet against multiple lists, which can substantially improve the performance of the firewall. However, these improvements provided by WFU techniques can be dwarfed by the performance degradation as the number of rules in the list becomes very large.

A key reason for the lack of scalability of most firewall implementations is due to the common use of linear search algorithms for comparing packets against a list of rules. In the worst case, a packet is matched at the last N^(th) rule in the list, so it must also be compared against all N−1 prior rules for a total of N comparisons. This poses a computational resource problem when the size of N is very large on a single processing node (including when such nodes are arranged in a data-, function-, hierarchical- or hybrid-parallel system), where the time required for processing each packet can quickly increase latency and reduce throughput to unacceptable levels. In fact, the WFU techniques provide good results in part because the reordering of, or the reduction in size of, rules on each processing node allows for a larger percentage of the total rules to reside in each processor's cache(s), which then substantially increases their performance relative to when only a small portion of those rules are cached.

The problem of searching firewall rule sets is well understood and highly researched, and there are some published techniques for sub-linear (substantially faster than linear) techniques applicable to firewall rules. However, these sub-linear techniques generally involve changing the underlying representation of rules. Examples of such an approach might be to use a graph, trie- or tree-like structure instead of a list to represent a set of rules, which would allow a match to be determined using tree search algorithms by traversing down the graph, trie or tree (see E. Fulp, Trie-Based Policy Representations for Network Firewalls, Proceedings of the IEEE International Symposium on Computer Communications, 2005 and Al-Shaer et al., Modeling and Management of Firewall Policies, IEEE Transactions on Network and Service Management, 2004). These approaches have potential but can add complexity or limitations that may reduce their practical usefulness in a commercial high performance firewall product.

SUMMARY

Adaptive packet filtering (APF), a set of techniques for processing firewall rules and packets, is described herein. APF offers improved processing performance compared to the WFU techniques in most cases, and can be combined with the WFU techniques or other parallel, pipelining and optimization techniques to achieve even greater performance.

The subject matter described herein includes methods, systems, and computer readable media for adaptive packet filtering. One method includes identifying at least one subset of rules in an ordered set of firewall packet filtering rules that defines a firewall policy such that the subset contains disjoint rules. Disjoint rules are defined as rules whose order can be changed without changing the integrity of the firewall policy. Rules in the subset are sorted to statistically decrease the number of comparisons that will be applied to each packet that a firewall encounters. Packets are filtered at the firewall using the sorted rules in the subset by using binary search, interpolated search, informed search, or hash lookup search algorithms to compare each packet to the sorted rules in the subset until the packet is allowed or denied and ceasing the comparing for the packet in response to the packet being allowed or denied and thereby achieving sub-linear searching for packets filtered using the sorted rules in the subset.

The subject matter described herein for adaptive packet filtering can be implemented in a non-transitory computer readable medium having stored thereon executable instructions that when executed by the processor of a computer control the computer to perform steps. Exemplary computer readable media suitable for implementing the subject matter described herein include chip memory devices, disk memory devices, programmable logic devices and application specific integrated circuits. In addition, a computer readable medium that implements a subject matter described herein may be located on a single device or computing platform or may be distributed across multiple devices or computing platforms.

Further, the subject matter described herein for adaptive packet filtering can be implemented on a particular machine, such as a network firewall including one or more network interfaces for receiving packets and packet filtering hardware and software for optimizing rules as described herein and for filtering packets using the optimized arrangement of rules.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the subject matter described herein will now be described with reference to the accompanying drawings of which:

FIG. 1 is a block diagram of a system for adaptive packet filtering according to an embodiment of the subject matter described herein;

FIG. 2 is a block diagram of application of the present subject matter to a pipelined processing approach according to an embodiment of the subject matter described herein;

FIG. 3 is a block diagram illustrating application of the present subject matter to a combination of pipelined and data parallel processing approaches according to an embodiment of the subject matter described herein;

FIG. 4 is a block diagram illustrating application of the present subject matter to a short-circuiting pipelined processing approach according to an embodiment of the subject matter described herein; and

FIG. 5 is a block diagram illustrating application of the present subject matter to a combination of pipelined and function parallel processing approaches according to an embodiment of the subject matter described herein.

DETAILED DESCRIPTION

Methods, systems, and computer readable media for adaptive packet filtering are disclosed. FIG. 1 is a block diagram illustrating an exemplary system for adaptive packet filtering according to an embodiment of the subject matter described herein. Referring to FIG. 1, a firewall 100 may function at the boundary between an external network and a protected network. Firewall 100 may include one or more network interfaces 102 for receiving packets from the external network. Firewall 100 may also include one or more network interfaces 104 for transmitting allowed packets to the protective network. In one implementation, firewall 100 may filter Internet protocol (IP) packets based on a combination of source and destination addresses in the IP headers of the packets. However, the subject matter described herein is not limited to filtering any particular protocol. Any packet network protocol with parameters for which firewall filtering rules can be defined is intended to be within the scope of the subject matter described herein.

As used herein, the term “firewall” includes any network security device or system of devices that inspects network traffic data that originates, terminates, or traverses the device system in any capacity and compares that traffic data (headers, payload, raw bits, etc.) to a set of one or more rules, signatures, or conditions, either inline (i.e., in real time) or offline (i.e., capture and replay of the traffic data). The term “firewall” is also intended to include an intrusion detection device that analyzes network traffic in real time or historically to detect the presence of intrusion events in a network. The term “firewall” is also intended to include a deep packet inspection device that analyzes network traffic in real time or historically to detect the presence of certain packet content in a network.

Firewall 100 includes a firewall rule subset identifier/rule sorter 106 for identifying at least one subset of rules in an ordered set of firewall packet filtering rules that defines a firewall policy such that the subset contains disjoint rules, where disjoint rules are defined as rules whose order can be changed without changing the firewall policy. Firewall rule subset identifier/rule sorter 106 may sort the rules in the subset or subsets to statistically decrease the number of comparison that will be applied to each packet that the firewall encounters. Exemplary methods for grouping and sorting rules will be described below.

Although in the example illustrated in FIG. 1 rule subset identifier/rule sorter 106 is illustrated as a component of firewall 100, the subject matter described herein is not limited to such an implementation. Rule subset identifier/rule sorter 106 can be implemented on any computing platform capable of sorting firewall rules using the methods described herein, and the sorted rule set can be provided to firewall 100 through any suitable means, such as communication over a network. In one implementation, rule subset identifier/rule sorter 106 may be implemented on a management platform separate from firewall 100.

Firewall 100 further includes a packet filter 108 for filtering packets at the firewall using the rules in the subset by using binary search, interpolated search, informed search, hash lookup search algorithms, or other sub-linear algorithms to compare each packet to each of the sorted rules in the subset until the packet is allowed or denied and ceasing the comparing for the packet in response to the packet being allowed or denied and thereby achieving sub-linear searching for the packets filtered using the sorted rules in the subset.

Once the subsets of disjoint rules have been identified by rule subset identifier/rule sorter 106, the rule subsets can be distributed across plural firewall processors in order to improve packet filtering efficiency. FIG. 2 is a block diagram illustrating an exemplary pipelined approach where rules and different subsets are distributed across plural firewall processors for processing packets in a pipelined manner. Referring to FIG. 2, firewalls 200 and 202 each include separate processors 204 and 206 for executing packet filters 108. In this example, rule subset identifier/rule sorter 106 identifies two rule subsets, subset A 208 and subset B 210. The rules within each subset 208 and 210 are disjoint and sorted to statistically decrease the number of comparisons that will be applied to each packet using the methods described herein. However, the rules in subset B 210 are dependent on the rules in rule subset A 208. Accordingly, rule subset identifier/rule sorter 106 distributes the rule across firewall processors 204 such that the rules in rule subset A 208 are applied before the rules in rule subset B 210. Because the rules in different subsets are distributed across plural processors in a pipeline manner, packet filtering efficiency is improved over a single-processor approach because the different processors can simultaneously apply rules to different packets. In the example illustrated in FIG. 2, packets that pass the filtering of rule subset A 208 are processed by processor 206, which applies rule subset B 210, at the same time that processor 204 applies rule subset A to new incoming packets.

In yet another embodiment, rule subset identifier/rule sorter 106 may distribute the grouped, sorted rules across firewall processors such that a combination of pipelined and data parallel processing techniques are used. FIG. 3 illustrates an example where firewalls 300, 302, 304, and 306 each include separate processors 308, 310, 312, and 314 for applying their respective packet filters. In the illustrated example, rule subset identifier/rule sorter 106 distributes rule subset A 316 to firewall 300, rule subset B 318 to firewalls 302 and 304, and rule subset C 320 to firewall 306. The rules within each subset A, B and C are disjoint. The rules in subset B are dependent upon the rules in subset A. The rules in subset C are dependent upon the rules in subsets A and B.

In operation, packets entering firewall 300 are filtered using rule subset A 316. The packets that are allowed by rule subset A 316 are divided between firewalls 302 and 304 such that the application of the rules in rule subset B 318 to different packets is performed in parallel. This is referred to as a data parallel approach. The packets that pass the filtering by rule subset B 318 are passed to firewall 306 for application of the rules in rule subset C 320. Accordingly, FIG. 3 illustrates an example where the rule subsets that are identified and sorted by rule subset identifier/rule sorter 106 are distributed across the firewall processors for a combination of pipelined and data parallel processing.

In yet another embodiment, the rules subsets that are identified and in which the rules are sorted using rule subset identifier/rule sorter 106 may be distributed across firewall processors in a short-circuiting pipelined manner. FIG. 4 is an example of short-circuiting pipelined filtering using rule subsets that are identified and sorted by rule subset identifier/rule sorter 106. Referring to FIG. 4, a first firewall 400 and a second firewall 402, respectively including processors 404 and 406, filter packets using packet filters 108. In the illustrated example, packet filter 108 uses rule subset A 408 and packet filter 108 uses rule subset B 410. Rule subsets A and B 408 and 410 may respectively implement different levels of a firewall hierarchy such that packets that pass the filtering by rule subset A 408 are allowed into the protected network. Packets that are identified by rule subset A 408 is requiring further filtering are distributed to rule subset B 410 for that filtering. Thus, rule subset identifier/rule sorter 106 can also be used with short-circuiting pipelined firewall techniques without departing from the scope of the subject matter described herein.

In yet another embodiment, rule subset identifier/rule sorter 106 may distribute the grouped, sorted rules across firewall processors such that a combination of pipelined and function parallel processing techniques are used. FIG. 5 illustrates an example where firewalls 500, 502, 504, and 506 each include separate processors 508, 510, 512, and 514 for applying their respective packet filters. In the illustrated example, rule subset identifier/rule sorter 106 distributes rule subset A 516 to firewall 500, rule subset B 518 to firewalls 502, and rule subset C 520 to firewall 504, and rule subset D 522 to firewall 506. The rules within each subset A, B, C and D are disjoint. The rules in subset B and C are dependent upon the rules in subset A. The rules in subset D are dependent upon the rules in subsets A, B and C.

In operation, packets entering firewall 500 are filtered using rule subset A 516. The packets that are allowed by rule subset A 516 are copied to both firewalls 502 and 504 such that the application of the rules in rule subsets B 518 and C 520 to the packets is performed in parallel. This is referred to as a function parallel approach. The packets that pass the filtering by rule subsets B 518 and C 520 are passed to firewall 506 for application of the rules in rule subset D 522. Accordingly, FIG. 5 illustrates an example where the rule subsets that are identified and sorted by rule subset identifier/rule sorter 106 are distributed across the firewall processors for a combination of pipelined and function parallel processing.

Technique

APF analyzes and orders the list of firewall rules in-place to contain functionally dependent groups, where each group contains a subset of rules that are disjoint, dependent or both, without substantially changing the underlying representation of rules and while preserving the aggregate security policy. APF then uses varying criteria to sort each group containing disjoint rules, then uses sub-linear search algorithms when comparing packets against the rules within that group. APF uses linear search algorithms when comparing packets within a group containing dependent rules or when otherwise appropriate. A detailed computational complexity analysis of APF would need to be completed. However, on average, it is hypothesized that only O(log(N)) comparisons would be needed to process a rule list of size N. In the theoretical best case when all rules are disjoint, this translates to about 20 comparisons (instead of 1,000,000) for a list of N=1,000,000 rules and about 30 comparisons for a list of N=1,000,000,000 rules. In the worst case when all rules are dependent, APF performs the same as linear search firewall cores. In practice, APF should process a packet against a very large list of rules (N=millions) in the same amount of time that other techniques can process against a very small list (N=hundreds, or thousands). APF does not inherently use parallel techniques; therefore, it can be combined with WFU techniques or other parallel/pipelining techniques to increase performance.

The following table shows preliminary results comparing a single linear search firewall) with a single APF core as the number of rules is increased.

linear firewall core APF core PPS = packets Latency in PPS = packets Latency in per second at microseconds at per second at microseconds at N = # rules in max throughout max throughput max throughout max throughput the core with 0 loss with 0 loss with 0 loss with 0 loss 1 844,595 12.3 811,688 12.3 10 730,994 11.9 766,871 12.3 100 314,861 13.9 718,391 12.0 1,000 29,357 44.6 683,060 12.0 10,000 930 1,112.4 464,684 12.0 100,000 Fail Fail 292,740 17.4 1,000,000 252,525 14.3 10,000,000 132,556 18.6

Detailed Technique

This section describes an exemplary algorithm for implementing the subject matter described herein.

A firewall rule is defined as an n-tuple criteria and an associated action for matching packets. For example, a 5-tuple rule that matches Internet Protocol version 4 (IPv4) packets might consist of 5 IPv4 header fields (source address, source port, destination address, destination port and protocol) and an action (allow, deny), and might specify the rule R1 as:

Dest Rule Source Addr Source Port Dest Addr Port Protocol Action R1 192.168.1.1 12345 10.1.1.1 80 TCP DENY

A firewall rule set is defined as an ordered list of n rules R1, R2, R3, . . . , Rn where the i in Ri is the index of the rule in the list. Packets that traverse the firewall are checked against each rule in the rule set until the first matching rule is found and its associated action is applied. An example rule set is S1 which contains:

Dest Proto- Rule Source Addr Source Port Dest Addr Port col Action Rl 192.168.1.1 12345 10.1.1.1 80 TCP DENY R2 192.168.2.2 ANY 10.2.2.2 25 TCP ALLOW R3 192.168.3.3 ANY 10.3.3.3 53 UDP ALLOW R4 ANY ANY 10.1.1.1 ANY ANY ALLOW R5 ANY ANY ANY ANY ANY DENY

An example TCP packet from source 192.168.4.4 port 54321 to destination 10.1.1.1 port 80 would be checked against but not match R1, R2 and R3; would be checked against and match R4 and be allowed; and, would not be checked against R5 because R4 was the first matching rule.

A firewall security policy is defined as the set of all possible packets that can traverse the firewall along with their specified outcomes as defined by the rule set. Changing the rules in a rule set usually results in a change of its security policy.

Within a rule set, a firewall rule is dependent on another rule if swapping the order of the two rules results in a change in the security policy of the rule set. Otherwise, the two rules are disjoint if swapping the order does not result in a change the security policy. For example, in rule set S1 above, rules R1 and R4 are dependent because placing R4 ahead of R1 would render R1 ineffective, thereby changing the security policy. Rules R1 and R2 are disjoint because placing R2 ahead of R1 does not change the security policy.

A permutation of a rule set is defined as a new rule set which contains the same rules as the original rule set, but which lists a different ordering of the rules from the original rule set without changing the original security policy. For example, in the rule set S1 above, swapping the order of the disjoint rules R1 and R2 would result in a permutation rule set S1′:

Dest Proto- Rule Source Addr Source Port Dest Addr Port col Action R2 192.168.2.2 ANY 10.2.2.2 25 TCP ALLOW R1 192.168.1.1 12345 10.1.1.1 80 TCP DENY R3 192.168.3.3 ANY 10.3.3.3 53 UDP ALLOW R4 ANY ANY 10.1.1.1 ANY ANY ALLOW R5 ANY ANY ANY ANY ANY DENY

Two rules are spatially disjoint if they are disjoint and their corresponding tuples are either identical or do not overlap. For example, in the rule set S1 above, rules R1 and R2 are disjoint but not spatially disjoint because the source ports 12345 and ANY overlap. However, rules R2 and R3 are both disjoint and spatially disjoint because the source ports ANY and ANY are identical, and the other 4 tuples do not overlap. (Other examples follow.)

A transform function is an algorithm that can be applied to a rule to create a sortable key for that rule, which can then be used to sort the rules by their keys using a key comparison function. For example, the transform function Tfn could concatenate the tuples of a rule into a bit array that is interpreted as a large integer, and a corresponding comparison function Cfn could be a simple integer comparison function. (Other examples follow.)

A rule subset is defined as an ordered grouping of one or more rules within a rule set. For example, in rule set S1 above, the rule subsets might be:

Subset Rules T1 R1, R2, R3 T2 R4 T3 R5

A rule group is defined as a rule subset with a group type (dependent, disjoint), transform function, comparison function, and a search algorithm hint (linear, sub-linear). The group type can be dependent if the group contains dependent rules, or can be disjoint if the group strictly contains disjoint rules. For example, the rule set S1 above might contain the following disjoint rule group:

Group Transform Comparison Group Type Rules Fn Fn Hint G1 Disjoint R1, R2, R3 Tfn Cfn Sub-linear

A rule set may be partitioned into a list of ordered rule groups such that the security policy of the rule set is not changed when each rule group is decomposed in the listed order. This partitioning is accomplished by applying a rule subset identification method to a given rule set. An example of such a method is:

-   -   1. For a given rule set S containing n rules R1, R2, . . . , Rn:         -   a. Create a new empty disjoint rule group Gj (initially G1)             in S.         -   b. Place the first ungrouped rule Ri (initially R1) into Gj.         -   c. For each remaining ungrouped rule Ri in S:             -   i. If Ri is disjoint from rules in Gj and placing Ri                 into Gj does not modify the security policy of S, then                 place Ri into Gj.             -   ii. Otherwise, leave Ri ungrouped.         -   d. If S contains ungrouped rules, then go to step 1.a.     -   2. The rule set S now contains m disjoint rule groups G1, G2,         G3, . . . , Gm which group together the n (possibly reordered)         rules R1, R2, . . . , Rn.         Applying the above method to rule set S1 might result in its         partitioning into the following list of disjoint ordered rule         groups:

Group Transform Comparison Group Type Rules Fn Fn Hint G1 Disjoint R1, R2, R3 Linear G2 Disjoint R4 Linear G3 Disjoint R5 Linear Decomposing the disjoint rule groups would result in G1, G2, G3 = [R1, R2, R3], [R4], [R5] = R1, R2, R3, R4, R5 = S1.

A partitioned rule set containing disjoint rule groups may then be sorted by applying a transform function to each rule within each disjoint group to derive a sortable key for each rule. Then, the rules may be reordered within their disjoint groups using their sortable keys. The resulting sorted groups may be searched using sub-linear searching algorithms. An example of the sorting method is:

-   -   1. For each disjoint group Gj in rule set S:         -   a. For each rule Ri in Gj:             -   i. Apply Transform Tfn to Ri to derive a sortable key                 Ki.         -   b. Sort the rules in Gj using the comparison function Cfn on             the sortable keys K.     -   3. The rule set S now contains m sorted disjoint rule groups G1,         G2, G3, . . . , Gm.         Applying the above method to the partitioned rule set S1 might         result in a new permutation rule set S1′ that contains the         following list of sorted rule groups where the ordering of rules         within G1 might change from R1, R2, R3 to R3, R1, R2:

Group Transform Comparison Group Type Rules Fn Fn Hint G1 Disjoint R3, R1, R2 Tfn Cfn Sub-linear G2 Disjoint R4 Tfn Cfn Sub-linear G3 Disjoint R5 Tfn Cfn Sub-linear

The permutated rule set containing disjoint rule groups may be consolidated to reduce the number of groups that contain a rule count at or below a certain threshold, such as 1 rule, by merging two or more consecutive disjoint groups into a larger dependent group that may be searched using linear searching algorithms. An example of the consolidation method is:

-   -   1. For each group Gj in rule set S:         -   a. If the sum of the number of rules in Gj and its             subsequent group Gj+1 is less than or equal to a specified             threshold (e.g. 1), then the two groups are merged, their             rules are concatenated, and the group type is set to             dependent.     -   2. The rule set S now contains m or fewer rule groups of both         dependent and disjoint types.         Applying the above method to the permuted rule set S1′ might         merge the disjoint group G3 into G2:

Group Transform Comparison Group Type Rules Fn Fn Hint G1 Disjoint R3, R1, R2 Tfh Cfn Sub-linear G2 Dependent R4, R5 (none) (none) Linear

The APF packet filtering method matches packets against a given rule set by sequentially iterating over each of the ordered rule groups, then performing the specified sub-linear or linear search within each group. An example of a rule filtering method is:

-   -   1. For each packet that traverses the firewall:         -   b. For each group Gj in rule set S:             -   i. If Gj is a dependent group, then perform linear                 search within that group until there is a first rule                 match.             -   ii. If Gj is a disjoint group:                 -   1. Apply transform T to the packet to derive a                     lookup key K.                 -   2. Use the comparison function C and sub-linear                     search within the group until there is a first rule                     match on key K.             -   iii. If there is a first rule match, then process the                 packet according to its specified action. Otherwise,                 continue to the next group Gj+1.

Additional Notes

-   -   The primary purpose of the partitioning of rules is to create         rule groups that, in aggregate, enable the fastest possible         searching of each packet against the rules in the rule set. In         most cases, the optimal partitioning should be the grouping of         maximal subsets of spatially disjoint rules. However, in some         cases where these disjoint groups are small (e.g. less than 10         rules), consolidating the disjoint groups into a single larger         group containing rules that are ordered using other criteria         (such as hit probabilities or hardware cache friendliness) and         employing linear or interpolated search algorithms may improve         performance. A critical concept is the flexibility to organize         the rules within the rule set in different ways that enable the         use of the most efficient and applicable search algorithm that         is available that accounts for the hardware capabilities, which         is the motivation behind the term “adaptive” in Adaptive Packet         Filtering.     -   Rule sets may be partitioned, sorted and consolidated in-place.     -   When partitioning a rule set into rule groups, each disjoint         group should generally contain the maximal subsets of disjoint         rules in order to reduce the number of disjoint groups in the         rule set.     -   When partitioning a rule set into rule groups and/or sorting         those groups, the transform and comparison functions may be         different for each rule group.     -   When partitioning a rule set into rule groups and/or sorting         those groups, the algorithms may account for the hit         probabilities of each rule and the aggregate hit probabilities         of each group.     -   When filtering packets, the search performed within any given         rule group may employ the fastest available search algorithm         applicable to that group even if it may be different from the         specified search algorithm hint.     -   When filtering packets, a sub-linear binary search within a         disjoint rule group may account for hit probabilities at each         pivot so that each recursion could maximize the probability of a         rule match.     -   When filtering packets, a constant-time search within a disjoint         rule group is possible by defining a hashing function as the         transform function such that the hash values for all rules         within a group are unique within that group.

EXAMPLES

This section provides examples of the following items described in the algorithm in the previous section.

1) Example of the rule breakup 2) Example of transform function 3) Definition of rule representation 4) Difference between sub-linear and linear 5) Explanation of disjoint rules

1) Examples of the Rule Breakup. Define

S1=(R1, R2, R3, R4, R5)

and

R1=from 1.2.3.4 to 3.4.5.6 deny

R2=from 2.3.4.5 to 4.5.6.7 allow

R3=from 3.4.5.6 to 5.6.7.8 allow

R4=from *.*.*.* to 3.4.5.6 allow

R5=from *.*.*.* to *.*.*.* deny

Then S1′ contains 3 groups of disjoint rules:

S1′=(G1, G2, G3)

G1=(R1, R2, R3)

G2=(R4)

G3=(R5)

Or, S1′ can contain 2 groups of disjoint and dependent rules:

S1′=(G1, G2)

G1=(R1, R2, R3), disjoint, sub-linear

G2=(R4, R5), dependent, linear

or

Define

S2=(R1, R2, R3, R4, R5, R6, R7, R8, R9)

and

R1=from 1.2.3.4 to 3.4.5.6 deny

R2=from 2.3.4.5 to 4.5.6.7 allow

R3=from 1.2.3.4 to 4.5.6.7 deny

R4=from 1.*.*.* to *.*.*.* allow

R5=from 3.4.5.6 to 5.6.7.8 deny

R6=from 2.*.*.* to *.*.*.* deny

R7=from *.*.*.* to 3.4.5.6 allow

R8—from *.*.*.* to 5.6.7.8 allow

R9=from *.*.*.* to *.*.*.* deny

Then S2′ contains 4 groups of “spatially disjoint” rules:

S2′=(G1, G2, G3, G4)

G1=(R1, R2, R3, R5)

G2=(R4, R6)

G3=(R7, R8)

G4=(R9)

Or, S2′ can contain 2 groups of disjoint and dependent rules:

S2′=(G1, G2)

G1=(R1, R2, R3, R5), disjoint, sub-linear

G2=(R4, R6, R7, R8, R9), dependent, linear

2) Example of Transform Function.

A rule R usually consists of an N-tuple, most basically a 3-tuple such as “from 1.2.3.4 to 3.4.5.6 deny”

tuple a=source IP address, e.g. “1.2.3.4”

tuple b=destination IP address, e.g. “3.4.5.6”

tuple c=action, e.g. “deny”

Each of these tuples have underlying scalar integer/bit vector representation, so in the above example:

source IP address=“1.2.3.4”=32 bit integer 0016909060

destination IP address=“3.4.5.6”=32 bit integer 0050595078

action=“deny”=8 bit integer 000

One possible transform function is a transform to scalar key which concatenates the digits of each of the tuples into a large integer value:

T(a, b, c)=abc

T(1.2.3.4, 3.4.5.6, deny)=0016909060 0050595078 000=00169090600050595078000

Another possible transform function is a transform to scalar key which concatenates the bits of each of the tuples into a large integer/bit vector:

a=1.2.3.4=00000001000000100000001100000100 (32 bits)

b=3.4.5.6=00000011000001000000010100000110 (32 bits)

c=0=00000000 (8 bits)

T(a, b, c)=abc=000000010000001000000011000001000000001100000100000001010000011 000000000

Another possible transform function is an identity function (i.e. transformation function that does not do anything), then defining a multi-dimensional comparison function for sorting purposes. An example of this is a comparison function that is radix-based for each tuple, which would essentially result in a rule set that is radix sorted by each tuple.

Note that the transform function must convert the rule into a sortable key, which does not necessarily have to be a scalar key (i.e. it can be a multi-dimensional key that uses a multi-dimensional comparison function for sorting).

3) Definition of Rule Representation.

Rule representation is the way a rule and a rule set are conceptually represented in software. The most common representation of a rule is as an N-tuple object or structure that simply holds all the tuples together:

  struct rule {  unsigned char ip_proto;  unsigned int ip_src_addr;  unsigned int ip_dst_addr;  unsigned short ip_src_port;  unsigned short ip_dst_port;  . . .  unsigned char action; };

The most common representation of a rule set is an array or linked list that holds the rules in a fixed order, and allows for iteration forwards and backwards in the array or list.

Example of Array:

memory location 0 1 2 3 4 5 6 7 8 value R1 R2 R3 R4 R5 R6 R7 R8 R9

Example of Linked List:

An alternate rule representation is to hold the rule and rule set in a trie or other graph structure. An example of this is described in “Balancing Trie-Based Policy Representations for Network Firewalls.” Stephen J. Tarsa and Errin W. Fulp. Proceedings of the IEEE International Symposium on Computer Communications, 2006.

Another alternate rule set representation is a hierarchical one as described in OPTWALL, described in Acharya et al., “OPTWALL: A Hierarchical Traffic-Ware Firewall,” available at:

http://www.isoc.org/isoc/conferences/ndss/07/papers/OPTWALL.pdf, where rule sets are broken down into mutually exclusive rule subsets which are arranged in a hierarchical order. Despite some similarities in terminology, OPTWALL and APF are different. For example, APF does not change the underlying rule or rule set representation. It simply reorders the rules in place and keeps track of the beginning and ending rules in each subset T externally from the rule or rule set. For example, say that a given rule is a standard structure and the rule set is an array form so that the rule set contains the following in the computer's memory:

Rule Set S:

Location 0 1 2 3 4 5 6 7 8 value R1 R2 R3 R4 R5 R6 R7 R8 R9 Suppose that the above rule set S can be divided into subsets containing disjoint rules:

T1=R2, R9, R1, R3

T2=R5, R7

T3=R2, R4, R8

so that

S′=T1, T2, T3=R2, R9, R1, R3, R5, R7, R2, R4, R8

Then the order of the rules in memory can be changed in place:

Rule Set S′:

The memory ranges for T1 (0-3), T2 (4-5) and T3 (6-8) are stored outside of the rule and rule set data structures.

4) Difference Between Linear and Sub-Linear.

A linear algorithm is one whose computational time increases linearly as the size of the set is increased. The best example of this is when looking up a word in a dictionary. If the dictionary is unsorted, then the order of the words would be arbitrary. Therefore, when looking up the word “zebra,” one could start from the beginning and search until the end to find it. If the dictionary contains 1,000 entries, you would need to examine all 1,000 words in the worst case.

A sub-linear algorithm is one whose computational time increases sub-linearly as the size of the set is increased. In the above example, if the dictionary were sorted alphabetically, then one could still use a linear search by starting from the beginning and searching until the end to find “zebra.” However, one could also use a sub-linear binary search algorithm by looking in the middle of the dictionary, then seeing if the middle entry comes alphabetically before or after (or is equal), then recursively selecting the middle of the appropriate half again and again to find the word “zebra.” Since at each recursion ½ of the remaining words are eliminated, it would take about log₂(1,000) or about 10 examinations to find the entry in the worst case.

Another example of a sub-linear algorithm is hashing. Suppose that the above dictionary contains only 5 letter words. If we define a hash function that sums the alphabet order of each letter in the word (z=26, e=05, b=02, r=18, a=01), then hash(“zebra”)=26+05+02+18+01=52. The computer could have an array containing all the words in the dictionary where each word's position in the array is the hash value of the word (subject to collisions). In the above example, the array's 52nd position would have the word “zebra,” so it would take only 1 comparison to determine a match without collision. This hash technique can be selectively used in APF.

5) Explanation of Disjoint Rules.

A rule R1 is “disjoint” from another rule R2 if their positions in the rule set S can be exchanged without altering the overall security policy. An example of this is rule set S containing:

R1=from 1.*.*.* to 2.3.4.5 deny

R2=from 1.2.3.4 to 1.2.3.4 allow

has the same security policy as rule set S′ containing:

R2=from 1.2.3.4 to 1.2.3.4 allow

R1=from 1.*.*.* to 2.3.4.5 deny

because P=P′; therefore, R1 and R2 are disjoint. The technique set forth above does not explain the concept of “spatially disjoint” rules. This is important if the transform function T cannot account for overlapping tuple values, which can be very common in practical settings.

A rule R1 is “spatially disjoint” from another rule R2 if they are “disjoint” and their corresponding tuples do not unevenly overlap (must be exactly equal, or do not overlap at all). In the above example, R1 and R2 are disjoint but not spatially disjoint because the first tuple of R1 (1.*.*.*) and R2 (1.2.3.4) are not equal but do overlap, i.e. the value of 1.2.3.4 would match the first tuple of both R1 and R2. An example of spatially disjoint rules are:

R3=from 1.2.3.4 to 3.4.5.6 deny

R4=from 2.3.4.5 to 3.4.5.6 allow

Here, the first tuple of R1 (1.2.3.4) and the first tuple of R2 (2.3.4.5) do not overlap, the second tuple of R1 (3.4.5.6) and R2 (3.4.5.6) are equal, and the >> third tuple of R1 (deny) and R2 (allow) do not overlap.

The importance of “spatially disjoint” rules is dependent upon the definition of a transform function T, so it may be possible to define T such that rules need not be “spatially disjoint” so long as rules are “disjoint.”

The disclosure of each of the publications referenced herein is hereby incorporated by reference in its entirety.

It will be understood that various details of the presently disclosed subject matter may be changed without departing from the scope of the presently disclosed subject matter. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation. 

What is claimed is:
 1. A method, comprising: identifying, from among a plurality of rules that define a packet filtering policy, one or more groups of disjoint rules, wherein each of the one or more groups of disjoint rules comprises at least two rules that when applied to a packet either allow the packet to continue toward its destination or prevent the packet from continuing towards its destination irrespective of an order in which the at least two rules are applied to the packet, a first of the at least two rules specifying a set of network addresses for which packets should be allowed to continue toward their respective destinations, and a second of the at least two rules specifying a set of network addresses for which packets should be prevented from continuing toward their respective destinations; receiving a plurality of packets; applying the first of the at least two rules to a first portion of the plurality of packets, the first portion of the plurality of packets having network addresses in the set of network addresses for which packets should be allowed to continue toward their respective destinations; applying the second of the at least two rules to a second portion of the plurality of packets, the second portion of the plurality of packets having network addresses in the set of network addresses for which packets should be prevented from continuing toward their respective destinations; allowing the first portion of the plurality of packets to continue toward their respective destinations; and preventing the second portion of the plurality of packets from continuing toward their respective destinations. 